Here is an excerpt from an article written by Jim Fountaine, Brian McCarthy, and Tamim Saleh for Harvard Business Review and the HBR Blog Network. To read the complete article, check out the wealth of free resources, obtain subscription information, and receive HBR email alerts, please click here.
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Artificial intelligence is reshaping business—though not at the blistering pace many assume. True, AI is now guiding decisions on everything from crop harvests to bank loans, and once pie-in-the-sky prospects such as totally automated customer service are on the horizon. The technologies that enable AI, like development platforms and vast processing power and data storage, are advancing rapidly and becoming increasingly affordable. The time seems ripe for companies to capitalize on AI. Indeed, we estimate that AI will add $13 trillion to the global economy over the next decade.
Yet, despite the promise of AI, many organizations’ efforts with it are falling short. We’ve surveyed thousands of executives about how their companies use and organize for AI and advanced analytics, and our data shows that only 8% of firms engage in core practices that support widespread adoption. Most firms have run only ad hoc pilots or are applying AI in just a single business process.
Why the slow progress? At the highest level, it’s a reflection of a failure to rewire the organization. In our surveys and our work with hundreds of clients, we’ve seen that AI initiatives face formidable cultural and organizational barriers. But we’ve also seen that leaders who at the outset take steps to break down those barriers can effectively capture AI’s opportunities.
Making the Shift
One of the biggest mistakes leaders make is to view AI as a plug-and-play technology with immediate returns. Deciding to get a few projects up and running, they begin investing millions in data infrastructure, AI software tools, data expertise, and model development. Some of the pilots manage to eke out small gains in pockets of organizations. But then months or years pass without bringing the big wins executives expected. Firms struggle to move from the pilots to companywide programs—and from a focus on discrete business problems, such as improved customer segmentation, to big business challenges, like optimizing the entire customer journey.
Leaders also often think too narrowly about AI requirements. While cutting-edge technology and talent are certainly needed, it’s equally important to align a company’s culture, structure, and ways of working to support broad AI adoption. But at most businesses that aren’t born digital, traditional mindsets and ways of working run counter to those needed for AI.
To scale up AI, companies must make three shifts
[Here’s the first]
From siloed work to interdisciplinary collaboration.
AI has the biggest impact when it’s developed by cross-functional teams with a mix of skills and perspectives. Having business and operational people work side by side with analytics experts will ensure that initiatives address broad organizational priorities, not just isolated business issues. Diverse teams can also think through the operational changes new applications may require—they’re likelier to recognize, say, that the introduction of an algorithm that predicts maintenance needs should be accompanied by an overhaul of maintenance workflows. And when development teams involve end users in the design of applications, the chances of adoption increase dramatically.
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Here is a direct link to the complete article.
Tim Fountaine is a partner in McKinsey’s Sydney office and leads QuantumBlack, an advanced analytics firm owned by McKinsey, in Australia.
Brian McCarthy is a partner in McKinsey’s Atlanta office and coleads the knowledge development agenda for McKinsey Analytics.
Tamim Saleh is a senior partner in McKinsey’s London office and heads McKinsey Analytics in Europe.